I built an agent memory layer that returns a "proof tree" with every answer - what it knew, when, and why

Been building this for a while and wanted to share it with people who actually run agents. The idea: most memory layers give your agent an answer and you just trust it. When recall is wrong, you can't see why it surfaced what it did. I wanted memory where every answer comes with its receipts - the exact memories used, when each was true (it's bi-temporal), what got superseded, and a hash so you can tell if anything changed. What works today: \- pip install aurra / npm install aurra \- bi-temporal versioning (query memory as it was at any past point) \- per-memory audit trail (extraction model, source, history) \- multi-tenant isolation \- BYO-LLM — pass your own provider key, costs stay yours It's a hosted API right now; self-host is on the roadmap, not built. Benchmarks are public with methodology + raw data (LongMemEval-S 80.2% mean; weakest category 33.9%, which I'm disclosing because the whole point is being honest about what it does and doesn't do). Genuinely after feedback from people building agents - where would this break for your use case? What's missing?